{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model creation. CNN for environmental sound classification\n",
    "-  In this notebook a CNN for Environmental Sound Classification is programmed. It was then trained on the public dataset UrbanSound8K, which consists of sounds corresponding to ten different classes.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies\n",
    "\n",
    "-  Tensorflow (version 1.1.0, when programmed)\n",
    "-  A package (imported below) that I created for a cleaner programming of the model. It is available on the repository. \n",
    "-  The dataset to train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sketch of the network arquitecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename = r\"C:\\Users\\Santiago\\Desktop\\Application Noise Map\\Master Thesis\\Pictures for Latex document\\Arquitecture_BuyllaNET.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's import some packages\n",
    "import santiago_my_modules_v3_16_04_18 as my\n",
    "import imp \n",
    "import math\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import time\n",
    "import librosa\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.python.framework import ops\n",
    "from cnn_utils import * # random mini batches\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's start with our Neural Network\n",
    "Here are some of the parameters of the model:\n",
    "> __Layers__ <br> 3 Conv layers + 3 Max-Pooling layers + 2 FC layers <br> layer1.shape = (5,5,1,24), that is f=5, n_C=1, #filters = 24; followed by __max-pooling__ (2x2,2x2) where (f,s), and ReLU activation <br> layer2.shape = (5,5,24,48), __max-pooling__ (2x2,2x2), ReLU activation function <br> layer3.shape = (5,5,48,48) ,__max-pooling__ (2x2,2x2), ReLU activation <br> layer4.shape = (64,2304), FC layer with 64 hidden units followed by ReLU activation function <br> layer5.shape = (10,64), 10 hidden units, Softmax activation function. <br> 'VALID' padding is used throughout the whole network \n",
    "<br><br> __Hyperparameters__ <br> Loss function: cross-entropy loss via mini-batch stochastic GD <br> Batch size: 50 TF patches, 3 s duration (randomly selected from the 4s of each audio clip) <br> Learning rate: 0.001 <br> Dropout: applied to the input of layers 4&5, with p = 0.5 <br> L2 regularization is applied to the weights of the last 2 layers, penalty factor = 0.0025<br>  Number of epochs: 50<br>Optimizer: Adam <br> <br>\n",
    "__Input to the network__ <br> TF patches from the log-scaled mel-spectogram representation of the audio signal. Duration = 3 seconds. <br> \n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset and normalize inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X = (60, 60, 130)\n",
      "Shape of Y = (60,)\n",
      "number of training examples = 16\n",
      "number of test examples = 22\n",
      "number of validation examples = 22\n",
      "X_train shape: (16, 60, 130, 1)\n",
      "Y_train shape: (16, 10)\n",
      "X_val shape: (22, 60, 130, 1)\n",
      "Y_val shape: (22, 10)\n",
      "X_test shape: (22, 60, 130, 1)\n",
      "Y_test shape: (22, 10)\n",
      "60\n"
     ]
    }
   ],
   "source": [
    "# LOAD DATASET\n",
    "X = np.load(r'C:\\Users\\Santiago\\deep-machine-learning\\UrbanSound8K_Treated\\deprueba\\features_mel.npy')\n",
    "Y = np.load(r'C:\\Users\\Santiago\\deep-machine-learning\\UrbanSound8K_Treated\\deprueba\\labels.npy')\n",
    "\n",
    "X = np.transpose(X, axes =(2,0,1))  # after this line: Shape [examples,60,130]\n",
    "print(\"Shape of X = \" +str(X.shape))\n",
    "print(\"Shape of Y = \" +str(Y.shape))\n",
    "\n",
    "# SPLIT DATASET INTO TRAINING/TEST SETS - I will train on 8 folders and use 1 for testing. \n",
    "files_per_folder = [22, 888, 925, 990, 936, 823, 838, 806, 816, 837] \n",
    "\n",
    "validation_folder = 0 #I won't use one of the folders since that is the one Bello&Piczak used for validation\n",
    "test_folder = 0\n",
    "X_train, Y_train, X_val, Y_val, X_test, Y_test  = my.split_dataset(X, Y, validation_folder, test_folder, files_per_folder)\n",
    "\n",
    "# GET RIGHT DIMENSIONS\n",
    "X_train = np.transpose(np.array(X_train, ndmin = 4), axes = (1,2,3,0))\n",
    "X_val = np.transpose(np.array(X_val, ndmin = 4), axes = (1,2,3,0))\n",
    "X_test = np.transpose(np.array(X_test, ndmin = 4), axes = (1,2,3,0))\n",
    "\n",
    "# NORMALIZE INPUTS TO HAVE MEAN = 0 AND VARIANCE = 1\n",
    "mean = np.mean(X_train, axis = 0)\n",
    "std = np.std(X_train, axis = 0)\n",
    "\n",
    "X_train = (X_train-mean)/std\n",
    "X_test = (X_test-mean)/std\n",
    "X_val = (X_val-mean)/std       # apply the same transformation to the validation and test data\n",
    "\n",
    "\n",
    "# ONE HOT ENCODING OF THE LABELS\n",
    "Y_train = np.transpose(convert_to_one_hot(Y_train, 10))\n",
    "Y_val = np.transpose(convert_to_one_hot(Y_val, 10))\n",
    "Y_test = np.transpose(convert_to_one_hot(Y_test, 10))\n",
    "\n",
    "\n",
    "# PRINT SOME INFORMATION ABOUT THE TRAIN/TEST SETS\n",
    "print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print (\"number of validation examples = \" + str(X_val.shape[0]))\n",
    "print (\"X_train shape: \" + str(X_train.shape))\n",
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"X_val shape: \" + str(X_val.shape))\n",
    "print (\"Y_val shape: \" + str(Y_val.shape))\n",
    "print (\"X_test shape: \" + str(X_test.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))\n",
    "\n",
    "print(X_train.shape[0]+X_test.shape[0]+X_val.shape[0])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X_train, Y_train, X_val, Y_val, X_test, Y_test, learning_rate = 0.009,\n",
    "          num_epochs = 100, minibatch_size = 64, print_cost = True, dropout = 1,\n",
    "          dropout_conv = 1, lambd = 0, index = 0, n_hidden_fc1 = 64, BN = False):\n",
    "    \"\"\"\n",
    "    Implements a three-layer ConvNet in Tensorflow:\n",
    "    CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> FLATTEN -> FULLYCONNECTED -> RELU -> SOFTMAX\n",
    "    \n",
    "    Arguments:\n",
    "    X_train -- training set, of shape (None, 60, 130, 1)\n",
    "    Y_train -- test set, of shape (None, n_y = 10)\n",
    "    X_val ---- validation set, of shape (None, 60, 130, 1)\n",
    "    Y_val ---- validation set, of shape (None, n_y = 10)\n",
    "    X_test --- test set, of shape (None, 60, 130, 1)\n",
    "    Y_test --- test set, of shape (None, n_y = 10)\n",
    "    learning_rate -- learning rate of the optimization\n",
    "    num_epochs -- number of epochs of the optimization loop\n",
    "    minibatch_size -- size of a minibatch\n",
    "    print_cost -- True to print the cost every 5 epochs\n",
    "    dropout -- probability of dropout regularization in the FC layers\n",
    "    dropout_conv -- probability of dropout in the conv layers\n",
    "    lambd -- L2 regularization term\n",
    "    index --- to keep track for debugging\n",
    "    n_hidden_fc1 -- number of hidden units of the first FC layer\n",
    "    BN -- Boolean. Whether to apply batch normalization or not\n",
    "    \n",
    "    Returns:\n",
    "    train_accuracy -- real number, accuracy on the train set (X_train)\n",
    "    test_accuracy --- real number, accuracy on the test set (X_test)\n",
    "    dev accuracy ---- real number, accuracy on the validation set (X_val)\n",
    "    parameters ------ parameters learnt by the model. They can then be used to predict.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    tf.reset_default_graph()                          # to be able to rerun the model without overwriting tf variables\n",
    "    tf.set_random_seed(1)                             # to keep results consistent (tensorflow seed)\n",
    "    seed = 3                                          # to keep results consistent (numpy seed)\n",
    "    \n",
    "    # Obtaining shapes from data\n",
    "    m, n_H0, n_W0, n_C0 = X_train.shape\n",
    "    n_y = Y_train.shape[1]             \n",
    "    \n",
    "    # Empty list to keep track of the cost per epoch\n",
    "    costs = []                                       \n",
    "    \n",
    "    # Create Placeholders \n",
    "    X, Y, keep_prob, keep_prob_conv, BN_istrain = my.create_placeholders(n_H0, n_W0, n_C0, n_y)\n",
    "\n",
    "    # Forward propagation: Build the forward propagation in the tensorflow graph\n",
    "    Z5, parameters = my.forward_propagation_with_dropout(X, keep_prob, keep_prob_conv, n_hidden_fc1,\n",
    "                                                         BN_istrain = BN_istrain, BN = BN)\n",
    "        \n",
    "    # Cost function: Add cost function to tensorflow graph\n",
    "    if lambd == 0:\n",
    "        cost = my.compute_cost(Z5, Y)\n",
    "    else:\n",
    "        cost = my.compute_cost_with_regularization(Z5, Y, parameters, lambd)\n",
    "\n",
    "    # Adam optimizer\n",
    "    with tf.name_scope('train'):\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)\n",
    "\n",
    "    # Initialize all the variables globally\n",
    "    init = tf.global_variables_initializer()\n",
    "    \n",
    "    # Create a save instance in order to save the trained variables and the graph definition\n",
    "    now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n",
    "    root_logdir = r'C:\\Users\\Santiago\\deep-machine-learning\\UrbanSound8K_Treated\\Deprueba\\Saved_Model' + str(index)\n",
    "    logdir0 = \"{}/saved_model_run-{}/\".format(root_logdir, now)\n",
    "    builder = tf.saved_model.builder.SavedModelBuilder(logdir0)\n",
    "    \n",
    "    # Creates a summary file writer\n",
    "    logdir = \"{}/event_run-{}/\".format(root_logdir, now)\n",
    "    file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n",
    "    \n",
    "    # Accuracy definition\n",
    "    with tf.name_scope('Accuracy'):   \n",
    "        correct_prediction = tf.equal(tf.argmax(Z5, 1), tf.argmax(Y, 1)) # Returns the truth value of (x == y) element-wise.\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))  # Casts a tensor to a new type.\n",
    "        \n",
    "        tf.summary.scalar('train_per_epoch_per_minibatch', accuracy)     # Creates tensorflow summary for training accuracy\n",
    "    \n",
    "    # Merge all the summaries created in the forward propagation graph creation\n",
    "    merged_summary = tf.summary.merge_all()\n",
    "    \n",
    "    # Start the session to compute the tensorflow graph\n",
    "    with tf.Session() as sess:\n",
    "        \n",
    "        # Run the initialization\n",
    "        sess.run(init)\n",
    "        \n",
    "        # Do the training loop\n",
    "        for epoch in range(num_epochs):\n",
    "\n",
    "            minibatches_cost = 0\n",
    "            seed = seed + 1\n",
    "            minibatches_train = random_mini_batches(X_train, Y_train, minibatch_size, seed)\n",
    "            num_minibatches_train = len(minibatches_train)\n",
    "\n",
    "            for minibatch in minibatches_train:\n",
    "\n",
    "                # Select a minibatch\n",
    "                (minibatch_X, minibatch_Y) = minibatch\n",
    "                \n",
    "                # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).\n",
    "                _ , minibatch_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X, Y:minibatch_Y, keep_prob:dropout,\n",
    "                                                                            keep_prob_conv:dropout_conv, BN_istrain:True})\n",
    "                # Cost per minibatch                \n",
    "                minibatches_cost += minibatch_cost    \n",
    "                \n",
    "            epoch_cost = minibatches_cost / num_minibatches_train \n",
    "            costs_summary = tf.Summary(value=[tf.Summary.Value(tag=\"tr_cost_per_epoch\", simple_value=epoch_cost)])\n",
    "            \n",
    "            validation_cost = sess.run(cost, feed_dict={X:X_val, Y:Y_val, keep_prob:1, keep_prob_conv:1, BN_istrain:False})\n",
    "            validation_summary = tf.Summary(value=[tf.Summary.Value(tag=\"validation_cost\", simple_value=validation_cost)])\n",
    "                        \n",
    "            validation_accuracy = sess.run(accuracy, feed_dict={X:X_val, Y:Y_val, keep_prob:1, keep_prob_conv:1, BN_istrain:False})\n",
    "            validation_acc_summary = tf.Summary(value=[tf.Summary.Value(tag=\"validation_acc\", simple_value=validation_accuracy)])\n",
    "            \n",
    "            \n",
    "            if print_cost == True and epoch % 5 == 0:\n",
    "                print (\"Cost after epoch %i: %f\" % (epoch, epoch_cost))\n",
    "                                \n",
    "                    \n",
    "            if epoch % 1 == 0:    \n",
    "                \n",
    "                # Save the cost to then plot it at the end\n",
    "                costs.append(epoch_cost)\n",
    "                \n",
    "                # Evaluate summaries\n",
    "                summary_str = merged_summary.eval(feed_dict={X:minibatch_X, Y:minibatch_Y, keep_prob:1,\n",
    "                                                             keep_prob_conv:1, BN_istrain:False})\n",
    "                \n",
    "                # Add summaries to the file_writer\n",
    "                file_writer.add_summary(summary_str, epoch)\n",
    "                file_writer.add_summary(costs_summary, epoch)\n",
    "                file_writer.add_summary(validation_summary, epoch)\n",
    "                file_writer.add_summary(validation_acc_summary, epoch)\n",
    "                \n",
    "                \n",
    "        # Add the meta graph and variables to the builder instance      \n",
    "        builder.add_meta_graph_and_variables(sess,\n",
    "                                       [tf.saved_model.tag_constants.TRAINING],\n",
    "                                       signature_def_map=None,\n",
    "                                       assets_collection=None)\n",
    "        \n",
    "        \n",
    "        # Save the builder instance, containing variables and graph\n",
    "        builder.save() \n",
    "        \n",
    "        # Close the file_writer for tensorboard\n",
    "        file_writer.close()\n",
    "\n",
    "        # Plot the cost\n",
    "        plt.plot(np.squeeze(costs))\n",
    "        plt.ylabel('cost')\n",
    "        plt.xlabel('epochs')\n",
    "        plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "        plt.show()\n",
    "     \n",
    "        # Let's calculate the accuracy previously defined above\n",
    "        # Split into batches for prediction, otherwise memory problems appear due to huge matrix sizes\n",
    "\n",
    "        train_accuracy = 0\n",
    "        \n",
    "        for minibatch_train in minibatches_train:\n",
    "            (minibatch_X_train, minibatch_Y_train) = minibatch_train\n",
    "            train_accuracy += accuracy.eval({X: minibatch_X_train, Y: minibatch_Y_train, keep_prob:1,\n",
    "                                             keep_prob_conv:1, BN_istrain:False})\n",
    "            \n",
    "        train_accuracy = train_accuracy / num_minibatches_train\n",
    "        test_accuracy = accuracy.eval({X: X_test, Y: Y_test, keep_prob:1, keep_prob_conv:1, BN_istrain:False})\n",
    "        dev_accuracy = accuracy.eval({X: X_val, Y: Y_val, keep_prob:1, keep_prob_conv:1, BN_istrain:False}) \n",
    "\n",
    "        print(\"Train Accuracy:\", train_accuracy)\n",
    "        print(\"Test Accuracy:\", test_accuracy)\n",
    "        \n",
    "                \n",
    "        return train_accuracy, test_accuracy, dev_accuracy, parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, run the whole thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after epoch 0: 2.726509\n",
      "Cost after epoch 5: 2.237848\n",
      "Cost after epoch 10: 2.268839\n",
      "Cost after epoch 15: 1.493516\n",
      "Cost after epoch 20: 1.232369\n",
      "Cost after epoch 25: 1.104339\n",
      "Cost after epoch 30: 1.032824\n",
      "Cost after epoch 35: 0.844535\n",
      "Cost after epoch 40: 0.896959\n",
      "Cost after epoch 45: 0.686003\n",
      "INFO:tensorflow:No assets to save.\n",
      "INFO:tensorflow:No assets to write.\n",
      "INFO:tensorflow:SavedModel written to: b'C:\\\\Users\\\\Santiago\\\\deep-machine-learning\\\\UrbanSound8K_Treated\\\\Deprueba\\\\Saved_Model6/saved_model_run-20180625074232/saved_model.pb'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14cf4fc0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.875\n",
      "Test Accuracy: 0.136364\n",
      "Time elapsed: 39.009233236312866\n"
     ]
    }
   ],
   "source": [
    "t = time.time()\n",
    "\n",
    "_, _, _, parameters = model(X_train, Y_train, X_val, Y_val, X_test, Y_test, learning_rate = 0.001,\n",
    "          num_epochs = 50, minibatch_size = 50, dropout = 0.5, dropout_conv = 1, lambd = 0.0025, \n",
    "                            n_hidden_fc1 = 64, index = 6, BN = False)\n",
    "\n",
    "        \n",
    "elapsed = time.time() - t\n",
    "\n",
    "\n",
    "print ('Time elapsed: %s' %elapsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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